System and method for real-time determination of a repetitive movement parameter

ABSTRACT

The invention pertains to a system for real-time determination of a parameter of a movement (PM, T p ) of repetitive form comprising:
         first means (EST 1 ) for estimating an approximation (T r ) of the period of said movement of repetitive form, before the end of the current movement, on the basis of representative signals (S 1 , S 2 ) indicative of said movement;   means (DET) for determining a size (F) of sliding window on the basis of said period (T r ) estimated by said first estimating means (EST 1 );   second means (EST 2 ) for precisely estimating, by sliding window, said movement parameter, on the basis of representative signals (S 1 , S 2 ) indicative of said movement and of said size (F) of sliding window delivered by said first estimating means (EST 1 ).

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a national phase application under 35 U.S.C. §371 ofPCT/EP2011/051961, filed Feb. 10, 2011, which claims priority to FrenchPatent Application No. 1050913, filed Feb. 10, 2010, the entire contentsof which both of these applications are expressly incorporated herein byreference.

BACKGROUND OF THE INVENTION 1. Field of the Invention

The invention pertains to a system and a method for real-timedetermination of a parameter of a movement of repetitive form.

The term real-time signifies that the response time is adapted to thecontext of the application.

Various embodiments of the present invention apply to any sector inwhich a movement of repetitive form takes place, such as the medicalsector, rehabilitation, the sports sector, but applies particularly wellto the video games sector, in which numerous games require movements ofrepetitive form on the part of the player. The various embodiments canalso apply to movements of repetitive form of an automaton or robot.

The analysis of human movements is implemented in various sectors suchas cinema, video games, and sport. This analysis makes it possible, forexample, to be able to reproduce the movements performed by a user, soas to animate a virtual depiction of a person displayed on a screen,without having to recreate a complex physical model, while retainingmore natural and spontaneous body actions.

The capture of movement is used principally for cinema and video gamesso as to animate avatars or virtual depictions of people on a displayscreen, by reproducing the real movements, such as is described forexample in the document WO 2008011352.

However, such systems require the use of specific markers disposed oneach limb of the user, combined with video acquisition centered on theperson. The video is thereafter analyzed so as to define a model bydetermining, in three dimensions, the location of each marker.

This technique involves a complex implementation since the constraintson the precision of the movements performed and the location of themarkers are significant. Moreover the acquisition of the data and theirprocessing are not necessarily performed in real time since thisprincipally entails reproducing the movements and not interacting inreal time with a system.

Other schemes, such as that disclosed in the document WO 2006103662,allow the tracking of sports events. This application makes it possibleto control a set of cameras in such a way as to improve theirorientation according to the phases of sports actions taking place, orto be able to perform a summary of the phases of sports actions thathave occurred. However, the cameras often being far from the sportsmen,the former cannot be used in applications requiring high precision.

In the case of interactions between a user and a machine, for example avideo games console, there exist other schemes currently underdevelopment which allow interaction between the player and the gamesconsole. Recently, project Natal from Microsoft (trademark) has beenpresented. It is aimed at proposing a new interface using a video camerawhich interprets the movements performed by the user so as to drive avideo game or multimedia interface.

However, this technique constrains the user to act in a determined spacerequiring notably the absence of any obstacle between the user and thesystem.

Other systems use onboard sensors, or, stated otherwise, the user isequipped with sensors, such as a satellite location system sensor,making it possible to give his location or position, or with sensorsmaking it possible to determine his movements, such as an accelerometer,a magnetometer, or a gyrometer. In certain embodiments, the sensors areminimally intrusive and easily positionable without outsideintervention.

Such sensors, even low cost ones, make it possible to obtain betterprecision in the measurement of movements than a video sensor where theprecision is proportional to the size and definition of the sensor andtherefore requires more complex processing and a more efficacious andmore expensive processing machine.

Onboard sensors may be used for geolocation and guidance. Certainvehicles or apparatuses equipped with satellite location systemreceivers, such as GPS receivers, also contain an inertial rig so as toalleviate temporary cutoffs of reception of the signals of the system.Such a system has thus been adapted for the guidance of pedestrians, asdescribed in the document US 2009192708. These approaches require,however, a global position reference, the movement sensors may be usedonly in a temporary manner so as to alleviate a consequent defect orelse to enhance precision. The systems used involve very few sensors, asimple accelerometer makes it possible to give the principal directionand the speed of the displacement, and no additional details arenecessary to characterize the movement. Moreover, it is not necessary torespond as quickly as possible upon a change of speed or direction. Suchsystems are not adapted for relatively precise movements, nor for theuse of an interface.

In various embodiments of the present invention, the user interacts witha machine solely by virtue of the movement sensors. This approachtherefore requires high precision, but above all the earliest possibleresponse and simple and natural handling.

The use of sensors fixed to a person so as to control a communicationinterface has expanded over recent years.

In particular in the context of video games where certain systems makeit possible to interact in a more precise manner than with a stick or akeyboard, but also to afford user-friendliness by enabling theuninitiated to play rapidly without learning to master controls, thoselinked to movements being more natural. Finally, this approach affordsimproved realism by adapting the video game to the player's ownmovements.

Thus, Sony has proposed a dualshock (trademark) stick compatible withits game console comprising a movement sensor, so that the movementsapplied to the console can be transcribed to the screen. However, themovements are limited and the principal functionality therefore remainsthe possibility of amplifying the directional movements as a function ofthe inclination of the stick.

More recently, Nintendo has introduced its mass-market Wii (trademark)console which comprises interactive sticks. These sticks are furnishedwith an accelerometer which is used to ascertain the movement of theplayer, but without more precisely determining the direction or theamplitude of the movement. To supplement this stick, a platform isdeveloped so as to also consider the movements of the legs, such asdescribed in document EP 0908701. However, this platform only makes itpossible to ascertain the frequency of leg movements at the samelocation and does not therefore tolerate any displacement of the player.Moreover, it does not make it possible to ascertain the orientation ofthe player so as to be able, for example, to perform a rotation command.

Document WO 2006086487 relates to the adaptation of a module to a sportsshoe so as to measure and transmit the information regarding thequantity of movements performed. The particular feature of thisinvention resides in the capacity to measure the quantity of physicalactivity performed by the player so as to be able to activate certainfunctionalities of the game, or certain characteristics of the avatar orvirtual depiction. However, this device does not allow completeinterfacing between a player and his virtual depiction. Indeed, only onesensor is used, for example an accelerometer, to measure the physicalactivity, but no information is recovered for more preciselycharacterizing the movements performed.

These systems lack precision and speediness for real-time applications,notably in the video games sector.

BRIEF SUMMARY OF THE INVENTION

An aim of various embodiments of the invention is to alleviate theproblems cited above.

According various embodiments of the invention, there is proposed asystem for real-time determination of a parameter of a movement ofrepetitive form comprising:

-   -   first means for estimating an approximation of the period of        said movement of repetitive form, before the end of the current        movement, on the basis of representative signals indicative of        said movement;    -   means for determining a size of sliding window on the basis of        said period estimated by said first estimating means; and    -   second means for precisely estimating, by sliding window, said        movement parameter, on the basis of representative signals        indicative of said movement and of said size of sliding window        delivered by said first estimating means.

The real-time determination is thus improved, since the first means forestimating an approximation of the period of the movement of repetitiveform, before the end of the movement in progress, allows the determiningmeans to estimate speedily, in an adaptive manner, a size of slidingwindow particularly adapted to the precise calculation of the movementparameter in a rapid manner. The size of the sliding window is thusautomatically adapted to the variation in the period of the movement ofrepetitive form. The expression movement of repetitive form is intendedto mean a movement of relatively similar form, but some of theparameters of which may vary, such as the period (or frequency orspeed), the amplitude, or the impact (power of a contact shock).

In various embodiments, said movement parameter is the period of saidmovement of repetitive form.

The system is particularly adapted for precisely estimating, in realtime, the period of the movement of repetitive form, on the basis of afirst approximate rapid estimation making it possible to rapidlydetermine a size of sliding window particularly adapted to a precisecalculation of the period, which is thus performed much more rapidly.Hence, the determination of the period of the movement of repetitiveform is thus performed precisely with a improved earliest possibleresponse time, i.e. an improved real-time aspect.

For example, said second estimating means comprise correlation-basedmeans for calculating the period of the movement of repetitive form.

Thus, on the basis of not very complex statistics, it is possible todetermine the parameter without a priori knowledge of the temporalsignature of the movement.

According to some embodiments, said first means for estimating anothermovement parameter of said movement of repetitive form, different fromsaid period, comprise said second means for precisely estimating saidperiod.

If the period of the movement of repetitive form is determined with asystem according to various embodiments of the invention, in real time,in a precise manner, said period may be used to calculate anothermovement parameter, different from the period, according to anotheraspect of various embodiments of the invention, by serving to determinea size of sliding window, thereafter allowing precise and rapidestimation of this other movement parameter.

In various embodiments, said determining means comprise a multiplicativeenhancement of safety.

A safety margin is thus taken for the determination of the size of thesliding window, thereby making it possible to prevent the size of thesliding window from being a little too small.

According to various embodiments, the system comprises a communicationinterface for communicating in real time the evolution of said movement,for example, an audiovisual interface.

Such an audiovisual communication interface is particularly well adaptedfor video games systems.

In various embodiments, the system comprises, furthermore, a sensorassembly adapted for being fixed to the element performing the movementof repetitive form, so as to deliver said signals.

The sensor assembly can comprise at least one magnetometer, and/or atleast one accelerometer, and/or a gyrometer, and/or a pressure sensor,and/or an electrocardiograph, and/or a flowmeter for measuring theamount of breath, and/or a sensor for measuring the frequency ofrespiration.

According to another aspect there is also proposed a method forreal-time determination of a parameter of a movement of repetitive formcomprising the steps consisting in:

-   -   estimating an approximation of the period of said movement of        repetitive form, before the end of the current movement, on the        basis of representative signals indicative of said movement;    -   determining a size of sliding window on the basis of said        approximately estimated period; and    -   estimating precisely, by sliding window, said movement        parameter, on the basis of representative signals indicative of        said movement and of said determined size of sliding window.

According to one mode of implementation, the precise period of saidmovement of repetitive form is determined in real time.

In one mode of implementation, the estimation of another movementparameter of said movement of repetitive form, different from saidperiod, uses in the step of approximately estimating said period, thestep of precisely estimating the period of said movement of repetitiveform.

According to one mode of implementation, said signals are transmitted bya sensor assembly fixed to the element performing the movement ofrepetitive form, comprising, for example, at least one magnetometer,and/or at least one accelerometer, and/or a gyrometer, and/or a pressuresensor, and/or an electrocardiograph, and/or a flowmeter for measuringthe amount of breath, and/or a sensor for measuring the frequency ofrespiration.

In various embodiments, a change of frame of said signals transmitted bythe sensor assembly furnished with a first orthonormal frame [X, Y, Z]is performed by using a decomposition into decreasing eigenvalues λ_(u),λ_(v) and λ_(w) to express said first orthonormal frame [X, Y, Z] in asecond orthonormal frame [U, V, W], whose axis U or whose axes U and Vcorrespond respectively to a principal axis or a principal plane of saidmovement.

Precision and robustness are thus improved. Furthermore, automaticcalibration of the sensor assembly is then possible.

According to various embodiments, said determination in real time of theprecise period of said movement of repetitive form detects local maximaand a global maximum, over said sliding window, said local and globalmaxima being multiples of an elementary duration, and selects themaximum, corresponding to said precise period, occurring earliest andwhose deviation with respect to said global maximum is less than athreshold.

The low risk of error in the detection of said precise period is thusstill more limited over the size of the sliding window already optimizedby virtue of the rapid approximate estimation of the period.

BRIEF DESCRIPTION OF THE DRAWINGS

The invention will be better understood on studying certain embodiments,described by way of wholly non-limiting examples, and illustrated by theappended drawings in which:

FIG. 1 schematically illustrates a system for real-time determination ofa parameter of a movement of repetitive form, according to one aspect ofvarious embodiments of the invention;

FIG. 2 schematically illustrates an exemplary embodiment of a system ofFIG. 1, in which the parameter is the period according to variousembodiments of the invention;

FIG. 3 schematically illustrates a system of FIG. 2 furthermoreestimating another movement parameter of said movement of repetitiveform, different from said period, according to various embodiments ofthe invention;

FIGS. 4 and 5 schematically illustrate a sensor assembly in the case ofa video game application;

FIGS. 6 and 7 schematically illustrate a problem of determining theperiod; and

FIG. 8 schematically represents an exemplary comparison of thecalculation of the period according to various embodiments of theinvention with an estimation with a window of optimized size.

DETAILED DESCRIPTION OF THE INVENTION

In the various figures, the elements having identical references areidentical.

In FIG. 1 is illustrated an example of a system for real-timedetermination of a parameter of a movement of repetitive form,comprising a first estimation module EST1 for estimating anapproximation Tr of the period of the movement of repetitive form,before the end of the current movement, on the basis of signals S1, S2representative of the movement. A determination module DET determines asize F of sliding window on the basis of said period Tr estimated by thefirst estimation module EST1, for example by multiplying the rapidapproximation Tr by a factor or gain equal to 1+Δ, so as to take asafety margin. Δ can for example lie between 0 and 0.5. A second modulefor precise estimation, by sliding window, of the movement parameter, onthe basis of the signals S1, S2 representative of the movement and ofthe size F of sliding window delivered by the first estimation moduleEST1.

FIG. 2 illustrates a case in which the movement parameter determined inreal time in a movement of repetitive form is the period of themovement, which is delivered as output Tp by the second estimationmodule EST2.

In this case, various embodiments of the invention make it possible,through the rapid estimation, generally before the end of the movementin progress, of the period of the movement Tr, to provide the secondmodule for precise estimation with a size F of sliding window which isoptimized, by way of the determination module DET, thereby allowing thesecond estimation module EST2 to perform, at the earliest possiblemoment, a precise estimation Tp of said period. This precise estimationis much more rapid.

In the case of the calculation of the period, the second estimationmodule EST2 performs a correlation by sliding window on the basis of asingle signal or of several signals, which may or may not comprise theinput signals of the first estimation module EST1.

FIG. 3 illustrates an embodiment “in cascade”, according to variousembodiments of the invention, for which the system of FIG. 2 isemployed, and serves as first rapid estimation module EST′1, forestimating a size of sliding window for precisely estimating anothermovement parameter which right away makes it possible to take as rapidestimation Tr′ of the period, directly the precise period Tp alreadycalculated in a precise manner.

The module for rapid estimation EST1 of the period can, for example,implement rapid estimations described in the document “Frequencytracking in nonstationary signals using Joint Order Statistics”,Proceedings of the International Symposium on Time-Frequency andTime-Scale Analysis 96, p 441-444, by A. Marakov, in “Un nouvel outild'analyse temps-frequence base sur un moyennage à recalage de phase” [Anew time-frequency analysis tool based on averaging with phaseregistration] Gretsi 2009, by M. Jabloun, or in “Adaptive spectrogramvs. Adaptive pseudo Wigner-Ville distribution for instantaneousfrequency estimation”, Signal Processing 2003, by S. Chandra Sekhar.

The signals S1, S2, or S3 may be identical, different, or one mayinclude another. They originate from sensors transmitting signalsrepresentative of a movement of repetitive form. For example, a sensorassembly can comprise at least one magnetometer, and/or at least oneaccelerometer, and/or a gyrometer, and/or a pressure sensor, and/or anelectrocardiograph, and/or a flowmeter for measuring the amount ofbreath, and/or a sensor for measuring the frequency of respiration.

FIG. 4 illustrates an example in which various embodiments of theinvention are applied to the video games sector in respect of walkingmovements of repetitive form. The sensor assembly comprises, for eachleg, a triaxial accelerometer and a triaxial magnetometer (X, Y and Z).Each modality is a three-dimensional signal, in this instance twelvesignals in total. These signals are sampled at regular intervals, andeach signal received is dated so as to be able to synchronize the data.

This configuration responds to a specific positioning of the sensor, forexample on the side of the foot so as to orient one of the principalaxes of a sensor in accordance with the movement, but it is conceivableto apply a processing to perform a change of frame (U, V, W), so as toposition the sensors in one and the same frame. This change of framealso makes it possible to ascertain the principal direction or theprincipal plane of the movement, as illustrated in FIG. 5.

Stated otherwise, a change of frame of the signals in a firstorthonormal frame (X, Y, Z) is performed by using a decomposition intodecreasing eigenvalues λ_(u), λ_(v) and λ_(W) to express the firstorthonormal frame (X, Y, Z) in a second orthonormal frame (U, V, W),whose axis U or whose axes U and V correspond respectively to aprincipal axis or a principal plane of said movement.

The signal having to contain a maximum of information in regard to themovement performed, the axis of the sensor from which the signal isextracted is oriented in an optimal manner with respect to the movement.

Either the sensor is positioned in an optimal manner, for example sothat the X axis of the sensor measures the component of the signalcomprising the most information, with the best signal/noise ratio, or achange of frame may be performed.

To perform this change of frame from the basis (X, Y, Z) to (U, V, W),an eigenvalue decomposition is used. The principle consists indetermining coefficients λ_(u), λ_(v) and λ_(w), such thatλ_(u)>λ_(v)>λ_(w). The basis of the sensor can thus be described as acombination of the various axes (U,V,W) which form an orthonormal frame.One of the properties of the decomposition makes it possible to definethis new frame such that the U axis is the principal axis of themovement.

For example, for a given foot, the 3 sensor signals, for example arisingfrom the accelerometer A: [Ax, Ay, Az], are considered over a window.

The correlation matrix C is be calculated thereafter:

${C = \begin{bmatrix}{c\left( {{Ax},{Ax}} \right)} & {c\left( {{Ax},{Ay}} \right)} & {c\left( {{Ax},{Az}} \right)} \\{c\left( {{Ay},{Ax}} \right)} & {c\left( {{Ay},{Ay}} \right)} & {c\left( {{Ay},{Az}} \right)} \\{c\left( {{Az},{Ax}} \right)} & {c\left( {{Az},{Ay}} \right)} & {c\left( {{Az},{Az}} \right)}\end{bmatrix}},$

with c(A,B) the inter-correlation function for A and B.

According to the principle of Principal Component Analysis or PCA, thecoefficients λ_(u), λ_(v) and λ_(W) are defined by the followingrelations:

${L = \begin{bmatrix}\lambda_{u} & 0 & 0 \\0 & \lambda_{v} & 0 \\0 & 0 & \lambda_{w}\end{bmatrix}},$

and D=P⁻¹CP; with P the switching matrix which thus defines the newframe, that is to say the matrix which makes it possible to modify thesignals so as to adapt them within the new frame. The decomposition intoeigenvalues makes it possible to determine the switching matrix P, aswell as the matrix L.

Thus, to apply the processing operations, it is possible to consider thesignal of the accelerometer A_(u), rather than A_(x).

For such an example, to simulate walking/running, the system considersthat the player performs walking/running movements, either when his feetleave the ground, or with the tips of his toes on the ground. In thesubsequent, nonlimiting, description, it is to this example that variousembodiments of the invention are applied.

The period makes it possible to define the frequency or speed of theplayer's paces, that is to say to determine whether the player iswalking slowly or quickly or whether he is running. The duration of thepaces may be estimated with an inter-correlation function for twosignals S1 and S2. The idea is to estimate the time shift between thetwo signals which maximizes their correlations, this shift τ_(opt) beingrelated thereafter to the period T of the player's paces. Variousconfigurations of increasing complexity may be envisaged:

S1=S2=Agx or Adx or Agu or Adu i.e. an autocorrelation on a component ofan arbitrarily chosen foot and if possible oriented along the principalmovement axis. In this case, τ_(opt)=T,

S1=Agx and S2=Adx, or Agu or Adu i.e. a correlation between two likecomponents of the two feet. In this case the two axes may have the samedirection and the same sense. In this case, τ_(opt)=T/2,

In the previous case, it is possible to be certain of the collinearityof the axes by carrying out an estimation of the principal axis of themovement on each foot by a technique of eigenvalue decomposition of thesignal.

To allow the tracking of the variations in the period of the player'spaces, the calculation of the correlation relies on a window of thesignal which considers in a causal manner, or stated otherwise whichtakes into account, a time window using only samples preceding theinstant of interest, the signal as well as the latest samples acquired.In order to determine the period of the signal, at least one period ofmay be used.

We have the following equation for the correlation Γ_(s1s2)(t,τ) betweentwo signals S1 and S2:

Γ_(S 1S 2)(t, τ) = ∫_(θ ∈ It) S 1(θ) ⋅ S 2(θ − τ) θ

in which:t represents the current instant,τ represents the shift considered,It represents the interval dependent on t.

This function may be estimated by:

${\Gamma_{S\; 1S\; 2}\left( {t,\tau} \right)} = {\sum\limits_{\theta = 0}^{{Tf} - \tau - 1}\; {S\; 1{\left( {t - \theta} \right) \cdot S}\; 2\left( {t - \theta - \tau} \right)}}$

T_(f) representing the size of the time window of interest and thereforedefining the span of variation of the delay τ as [0; Tf[.

In order to favor the latest samples acquired and to improve the qualityof the estimation, it is possible to add within this calculation aweighting window w_(Tf-τ), of duration T_(f)-τ which thus makes itpossible to apply to each window of the signal a certain weightproportional to the instant of the window and the instant of the sample.The correlation function can then be calculated through the followingrelation:

${\Gamma_{S\; 1S\; 2}\left( {t,\tau} \right)} = {\frac{1}{\sum\limits_{\alpha = 0}^{T_{f} - \tau - 1}\; {w_{T_{f} - \tau}(\alpha)}^{2}} \times {\sum\limits_{\theta = 0}^{T_{f} - \tau - 1}{S\; 1{\left( {t - \theta} \right) \cdot S}\; 2\left( {t - \theta - \tau} \right){w_{T_{f} - \tau}^{2}(\theta)}}}}$

in which α represents a temporal index of the weighting window.

The correlation makes it possible to determine, when the latter is amaximum, the optimal shift corresponding to the period of the signal.FIG. 6 represents the correlation function as a function of t and of τfor a signal whose period varies.

FIG. 7 is a section through FIG. 6 at a given instant t=35s. These twofigures involve the case S1=S2=Agx or Adx with an autocorrelation on acomponent of a foot. Thereafter, the maximum may be tracked so as tocorrect any errors at a given instant.

This step thus makes it possible to facilitate the search for themaximum correlation value by removing the secondary values, multiples ofthe period, by virtue of the suitably adapted size of the slidingwindow.

The result of period corresponding to the duration of a pace istherefore the value T determined by the maximum of the correlation:

$T = {\max\limits_{\tau}\left( {\Gamma \left( {t,\tau} \right)} \right)}$

This scheme therefore uses a single parameter: τ_(max)(T_(f))corresponding to the maximum shift that is considered for thecalculation of the correlation. This shift may be interpreted as themaximum possible delay for a time window of this size.

When using a fixed parameter, the delay will be constant, but if therate accelerates, it is not possible to respond at the earliest possiblemoment. Accordingly, the maximum size of the window may be adjusted as afunction of the slowest pace, i.e. several seconds. Thus the idea is tohave an adaptive adjustment of the size of the analysis window so as tooptimize the speediness of the system upon a change of walking rate.

Stated otherwise, as shown by FIG. 7, the result of the correlation Γexhibits several lobes, each maximum of which is located at T, 2T, 3T, .. . , kT in the case of the autocorrelation (respectively T/2, 3T/2,5T/2, . . . , (2k+1)T/2 in the case of the correlation between the twofeet). In the ideal case of a perfectly stationary signal, the lobewhose maximum is the global maximum is situated at T (respectively T/2).However, in the case of a signal whose period is non-stationary, theglobal maximum might not be situated at T (respectively T/2), but at 2Tor 3T (respectively 3T/2, 5T/2).

In this case, the result given for the estimation of the period wouldtherefore be wrong, and the estimated period would be two or three timesgreater than the real value. Hence, a correction is afforded as follows:

Let T be the instant of the global maximum.

If there exists a lobe whose maximum is close to the value of themaximum at the instant T/2 or T/3, or T/4 . . . then the instant T iscorrected as T/2 or T/3 or T/4, that is to say the largest possibledenominator (respectively 2T/5, 2T/3, . . . ).

A second check is possible, so as to validate the temporal continuity.The principle consists in validating the value of T, by comparing itwith values which are determined over the latest windows. If the valuevaries too much, then we wait for the acquisition of a new sample.According to the value of T on the following window, which is eitherclose to the values on the preceding windows, or close to T on thecurrent window. In the latter case, there has been a change of rate anda good value of T has been found, otherwise, the movement has not variedand the estimation of T over the current window is false, and may bereplaced with the average of T over the previous window and thefollowing window.

According to various embodiments of the invention, a step ofpredetermining the size of the sliding window is added, which is basedon a simple and approximate estimation of the period of the signal.

For this simple and rapid estimation, it is for example possible toimplement the estimation of Makarov, cited above, which makes itpossible to define in a causal manner, i.e. using only samples from thepast, a value scale for the period of the signal.

A Marakov estimation makes it possible to determine the frequency of anon-stationary signal, at each instant, by considering the latestsamples acquired. The principle relies on the statistics of trends. Foreach point of the signal, it is possible to estimate whether the trendis modified, that is to say whether the maximum and minimum valuesobserved at the previous instant are retained. If this trend ismodified, then the point is not an extremum, otherwise it is anextremum. Thus, it is possible to ascertain at each instant the instantcorresponding to the previous extremum, which may be converted, knowingthe sampling frequency, into an estimated period or frequency.

The algorithm is of reduced complexity and is therefore perfectlyadapted to a prior determination of the period of the signal. On thebasis of this estimation, it is indeed possible to determine in anoptimal manner the size of the sliding window on which the processingoperations for estimating the parameters are subsequently performed. Itis indeed more relevant to consider only the latest periods of thesignal which correspond to the movement in progress, rather than toconsider signals describing a past and completed movement which woulddisturb the calculations.

For example, the signals arising from the magnetometers are used asinput for the first estimation module EST1 since they comprise fewerlobes. This Marakov estimation simply considers the changes of trend ofthe signal by evaluating the presence of extrema. For each point, it ispossible to determine the distance from the closest past extremum. Thuson the appearance of a new extremum it is possible to estimate theperiod by virtue of the distance from the previous extremum.

A second parameter that may be estimated is the amplitude, that is tosay the length of a pace. Likewise, it is possible to extract the sizeof the sliding window containing the signal of the pace performed,either by using the result of the precise correlation of the secondestimation module EST2, or on the basis of the Makarov result of thefirst rapid estimation of the first estimation module EST1.

The amplitude is determined with the aid of the signals arising from themagnetometers and corresponds to the absolute value of the difference ofthe maximum and of the minimum of the sum of the signals:

${A(t)} = {{\underset{i \in {\lbrack{{t - T},t}\rbrack}}{\max \mspace{11mu} {S(i)}} - \underset{i \in {\lbrack{{t - T},t}\rbrack}}{\min \mspace{11mu} {S(i)}}}}$

S being the sum of the signals of the magnetometers of the right foot orof the left foot:S(t)=M_(x)(t)+M_(y)(t)+M_(z)(t) or S(t)=M_(u)(t)+M_(v)(t)+M_(w)(t) inthe case of a change of frame.

A third parameter may be the impact, that is to say the power of theshock between the heel and the ground. This value is linked directly tothe acceleration of the sensor and therefore proportional to thesevalues.

It is considered that the result corresponds to the maximum value of theacceleration during the pace performed. Just as for the amplitude, it istherefore possible to extract the window of the signal corresponding tothe pace performed, either with the aid of the result of the precisecorrelation of the second estimation module EST2, or on the basis of theMakarov result of the first rapid estimation of the first estimationmodule EST1.

The calculation is simply performed as being the norm of theacceleration signals:

${I(t)} = {{\max\limits_{i \in {\lbrack{{t - T},t}\rbrack}}{{A(i)}}} = {\max\limits_{i \in {\lbrack{{t - T},t}\rbrack}}\sqrt{{A_{x}(t)}^{2} + {A_{y}(t)}^{2} + {A_{z}(t)}^{2}}}}$

Another characteristic of the movement may be the orientation.Accordingly, the signals of the magnetometers are used, by consideringmost naturally the orientation of the player with respect to magneticNorth.

The correlation calculation involves a weighting window. The form of theweighting may be chosen. However, the form has little influence onperformance since the window accords greater significance to the mostrecent samples.

Finally, depending on the position of the sensor, it may be beneficialto indicate the principal axis of orientation of the sensors so as touse only two signals (for example left foot and right foot along the Xaxis) for the calculation of the correlation.

It is also possible to apply a change of frame so as to determine theprincipal axis of the movement and consequently to automaticallydetermine the principal axis to be considered, but this calculation maylead to additional complexity. Here again, experiments have highlightedthat the use of the same X axis, when the sensor is positioned on theside of the foot, makes it possible to provide good results, whileretaining good robustness.

In order to illustrate the benefit of predetermining the walkingfrequency, FIG. 8 represents the results for the determination of theperiod or duration of the paces by using an adaptive or non-adaptivewindow.

The signals, represented from top to bottom, are:

-   -   the signal of acceleration along the X axis transmitted by the        accelerometer linked to a foot;    -   the correlation function as a function of the time t and of the        shift t for a variable window size. The black zone at the top of        the image is linked to the size of the window adjusted in an        adaptive manner. It varies, in this example, between 0.5 seconds        and 1.7 seconds;    -   the period estimated in an adaptive manner according to various        embodiments of the invention, in comparison to the real value;    -   the correlation function as a function of the time t and of the        shift τ for a fixed window size; and    -   the period estimated with a window of fixed size of 2 seconds,        in comparison to the real value.

Thus at the instants A, B and C, at which a transition from rapidwalking to slow walking takes place, the adaptive approach representedin the second graph responds more rapidly than in the case, representedin the third graph, of a window of fixed size. The instant D correspondsto a transition from slow walking to rapid walking for which thedifference is more subtle on account of the signal itself but thereactivity is nevertheless improved. The instant E is interesting sinceit shows that during a gentler transition between two walking speeds ismore gradual with an adaptive approach.

Various embodiments of the present invention are particularly beneficialfor improving the real-time aspect, as well as, in the case of videogaming, for improving the robustness of the system in relation to thevarious ways of playing.

1. A system for real-time determination of a parameter of a movement ofa repetitive form, comprising: first means for estimating anapproximation of a period of said movement of the repetitive form,before an end of a current movement, on a basis of representativesignals indicative of said movement; means for determining a size of asliding window on a basis of said period estimated by said first meansfor estimating; and second means for precisely estimating, by saidsliding window, said movement parameter, on said basis of representativesignals indicative of said movement and of said size of sliding windowdelivered by said first estimating means.
 2. The system of claim 1, inwhich said movement parameter is the period of said movement ofrepetitive form.
 3. The system of claim 2, in which said secondestimating means comprises a correlation-based calculation means.
 4. Thesystem of claim 2, in which a third means for estimating anothermovement parameter of said movement of repetitive form, different fromsaid period, comprises said second means for precisely estimating saidperiod.
 5. The system of claim 1, in which said determining meanscomprises a multiplicative enhancement of safety.
 6. The system of 1,further comprising a communication interface for communicating in realtime the evolution of said movement.
 7. The system of claim 6, in whichsaid communication interface comprises an audiovisual interface.
 8. Thesystem of claim 1, further comprising a sensor assembly adapted forconnecting to an element performing the movement of repetitive form, soas to deliver at least the representative signals.
 9. The system ofclaim 8, in which said sensor assembly includes at least onemagnetometer; accelerometer, gyrometer, pressure sensor,electrocardiograph, flowmeter for measuring breath, or a sensor formeasuring respiration.
 10. A method for real-time determination of aparameter of a movement of a repetitive form comprising: estimating anapproximation of a period of said movement of the repetitive form,before an end of a current movement, on the basis of representativesignals indicative of said movement; determining a size of a slidingwindow on a basis of said approximation; and estimating precisely, bythe sliding window, said parameter, on the basis of at least onerepresentative signal indicative of said movement and of said determinedsize of the sliding window.
 11. The method of claim 10, furthercomprising determining the precise period of said movement of therepetitive form in real time.
 12. The method of claim 11, furthercomprising estimating another movement parameter of said movement of therepetitive form by precisely estimating the period of said movement ofthe repetitive form.
 13. The method of claim 11, in which the signalsare transmitted by a sensor assembly connected to an element performingthe movement of the repetitive form.
 14. The method of claim 13, inwhich said signals transmitted by the sensor assembly originate from atleast one magnetometer, accelerometer, gyrometer, pressure sensor,electrocardiograph, flowmeter for measuring breath, or sensor formeasuring respiration.
 15. The method of claim 13, in which a change ofa frame of said signals transmitted by the sensor assembly furnishedwith a first orthonormal frame is performed by using a decompositioninto decreasing eigenvalues to express said first orthonormal frame in asecond orthonormal frame, whose at least one axis corresponds to atleast a principal axis of said movement.
 16. The method of claim 11,further comprising detecting local maxima and a global maximum, oversaid sliding window, said local and global maxima being multiples of anelementary duration; and selecting the maximum occurring earliest andwhose deviation with respect to said global maximum is less than athreshold.